Differently from Random Forests, recent deep learning techniques are able to autonomously learn
how to extract information from raw data, deciding by themselves which patterns of the
dataset are meaningful for the task addressed. Convolutional neural networks (CNNs) [1]} are particular deep learning methods that show their full power in image recognition. They look for relevant features of the (raw) image by performing several convolutions with small square filters, and in the end convert the pixel information into the abstract information at the output layer. This output is a prediction of the quantity of interest (in our case gammaness, energy or arrival direction).
